package net.bwie.realtime.jtp.dws.log.job;

import com.alibaba.fastjson.JSON;
import net.bwie.realtime.jtp.dws.log.bean.PageViewBean;
import net.bwie.realtime.jtp.dws.log.function.PageViewBeanMapFunction;
import net.bwie.realtime.jtp.dws.log.function.PageViewWindowFuncton;
import net.bwie.realtime.jtp.utils.DorisUtil;
import net.bwie.realtime.jtp.utils.KafkaUtil;
import org.apache.flink.api.common.eventtime.SerializableTimestampAssigner;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.datastream.WindowedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;

import java.time.Duration;

/**
 * 当日APP流量日志数据进行实时汇总统计，按照分钟级别窗口汇总计算
 *  粒度：地区、ba品牌、ch渠道、is_new新老访客，
 *  指标：PV（页面浏览数）、浏览总时长、UV（唯一访客数）、SV（会话数）
 * @author xuanjy
 * @date 2025/8/18
 */
public class JtpTrafficpageViewMinuteWindowDwsJob {
    public static void main(String[] args) throws Exception{
        // 1.执行环境-env
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        env.enableCheckpointing(5000L);

        // 2.数据源-source
        DataStream<String> pageStream = KafkaUtil.consumerKafka(env, "dwd-traffic-page-log");
//        kafkaDataStream.print("kafka");
        // 3.数据转换-transformation
        DataStream<String> resultStream = handle(pageStream);
        resultStream.print("result");
        // 4.数据输出-sink
        DorisUtil.saveToDoris(resultStream, "jtp_realtime_report", "dws_traffic_page_view_window_report");
        // 5.触发执行-execute
        env.execute("JtpTrafficPageViewMinuteWindowDwsJob") ;
    }
    /**
     *DWS汇总层，对页面浏览日志数据进行汇总计算 其中分组keyBy、窗口window计算
     */
    private static DataStream<String> handle(DataStream<String> stream) {
        //s1-按照mid设备ID分组 用于计算uv 使用状态State记录今日是否第一次访问
        KeyedStream<String, String> midStream = stream.keyBy(
                value -> JSON.parseObject(value).getJSONObject("common").getString("mid")
        );
        // s2-将流中每条日志数据封装试题类Bean对象
        DataStream<PageViewBean> beanStream = midStream.map(new PageViewBeanMapFunction());

        // s3-事件时间字段和水位线
        SingleOutputStreamOperator<PageViewBean> timeStream = beanStream.assignTimestampsAndWatermarks(
                WatermarkStrategy.
                        <PageViewBean>forBoundedOutOfOrderness(Duration.ofSeconds(0))
                        .withTimestampAssigner(
                                (SerializableTimestampAssigner<PageViewBean>) (element, recordTimestamp) -> element.getTs()
                        )
        );
        //s4-分组keyBy,ar地区、ba品牌、ch渠道、is_new新老访客
        KeyedStream<PageViewBean, String> keyedStream = timeStream.keyBy(
                bean -> bean.getBrand() + "," + bean.getChannel() + "," + bean.getProvince() + "," + bean.getIsNew()
        );
        //s5-开窗：滚动窗口 窗口大小为1分钟
        WindowedStream<PageViewBean, String, TimeWindow> windowStream = keyedStream.window(
                TumblingEventTimeWindows.of(Time.minutes(1))
        );

        //s6-聚合:对窗口中数据计算
        SingleOutputStreamOperator<String> reportStream = windowStream.apply(new PageViewWindowFuncton());


        //s7-返回窗口中数据计算
        return reportStream;
    }

}
